Triple
T26934804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Imperial Quarter of Metz |
E678342
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Place du Général de Gaulle
Place du Général de Gaulle is a prominent square in Metz, France, known for its grand imperial-era architecture and central role in the city's urban and transport network.
|
E1757418
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Place du Général de Gaulle | Statement: [Imperial Quarter of Metz, hasPart, Place du Général de Gaulle]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Place du Général de Gaulle Triple: [Imperial Quarter of Metz, hasPart, Place du Général de Gaulle]
Generated description
Place du Général de Gaulle is a prominent square in Metz, France, known for its grand imperial-era architecture and central role in the city's urban and transport network.
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69eeeb4cac908190a45956c2993d1cc2 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f6204d3b948190a5215f942e5e8723 |
completed | May 2, 2026, 4:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a1247e6bdb881908af05437e8c64c20 |
completed | May 24, 2026, 12:35 a.m. |
| NEDg | Description generation | batch_6a1248bb58a48190ae84e7b538b10503 |
completed | May 24, 2026, 12:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a124973e3c88190898b0cece69419b3 |
completed | May 24, 2026, 12:42 a.m. |
Created at: April 27, 2026, 6:15 a.m.